Abstract
BackgroundDental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, plaque detection and control are extremely important for children’s oral health. The objectives of this study were to design a deep learning-based artificial intelligence (AI) model to detect plaque on primary teeth and to evaluate the diagnostic accuracy of the model.MethodsA conventional neural network (CNN) framework was adopted, and 886 intraoral photos of primary teeth were used for training. To validate clinical feasibility, 98 intraoral photos of primary teeth were assessed by the AI model. Additionally, tooth photos were acquired using a digital camera. One experienced pediatric dentist examined the photos and marked the regions containing plaque. Then, a plaque-disclosing agent was applied, and the areas with plaque were identified. After 1 week, the dentist drew the plaque area on the 98 photos taken by the digital camera again to evaluate the consistency of manual diagnosis. Additionally, 102 intraoral photos of primary teeth were marked to denote the plaque areas obtained by the AI model and the dentist to evaluate the diagnostic capacity of each approach based on lower-resolution photos. The mean intersection-over-union (MIoU) metric was employed to indicate detection accuracy.ResultsThe MIoU for detecting plaque on the tested tooth photos was 0.726 ± 0.165.The dentist’s MIoU was 0.695 ± 0.269 when first diagnosing the 98 photos taken by the digital camera and 0.689 ± 0.253 after 1 week. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after 1 week. When the dentist and the AI model assessed the 102 intraoral photos, the MIoU was 0.652 ± 0.195 for the dentist and 0.724 ± 0.159 for the model. The results of a paired t-test found no significant difference between the AI model and human specialist (P > .05) in diagnosing dental plaque on primary teeth.ConclusionsThe AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist. This finding illustrates the potential of such AI technology to help improve pediatric oral health.
Highlights
Dental plaque causes many common oral diseases
The mean intersection-over-union (MIoU) for the detection of dental plaque on the testing tooth photos was 0.726 ± 0.165 when 709 photos were used for training, and 177 photos were used for testing
The MIoU of the dentist when diagnosing the 98 photos taken by the digital camera for the first time was
Summary
Dental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Dental plaque is a precursor to many oral diseases (e.g., caries, gingivitis, and periodontitis) [1]; its detection is important for maintaining children’s oral health [2, 3]. Dental plaque is detected by clinicians using either an explorer or with the aid of a disclosing solution and is quantified using indices based on the area of tooth covered or the plaque thickness [5, 6]. These assessment methods are inconvenient and time consuming, especially when the children are not cooperative. There is a need to develop a cost-effective and convenient technique to objectively detect and quantify dental plaque
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